Hadoop and HBase workloads tend to vary a lot and it takes experience to correctly anticipate the amounts of storage, processing power, and inter-node communication that will be required for different kinds of jobs.

This document provides insights on choosing the appropriate hardware components for an optimal balance between performance and both initial as well as the recurring costs. (For a brief summary of the hardware sizing recommendations, see Conclusion.)

Hadoop is a software framework that supports large-scale distributed data analysis on commodity servers. Hortonworks is a major contributor to open source initiatives (Apache Hadoop, HDFS, Pig, Hive, HBase, ZooKeeper) and has extensive experience managing production level Hadoop clusters. Hortonworks recommends following the design principles that drive large, hyper-scale deployments. For a Hadoop or HBase cluster, it is critical to accurately predict the size, type, frequency, and latency of analysis jobs to be run. When starting with Hadoop or HBase, begin small and gain experience by measuring actual workloads during a pilot project. This way you can easily scale the pilot environment without making any significant changes to the existing servers, software, deployment strategies, and network connectivity.